{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76a970a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn\n",
    "seaborn.set_style(\"whitegrid\")\n",
    "\n",
    "update_figs = True\n",
    "\n",
    "from procedures import dh, FindEffort\n",
    "from procedures import FindSWF2, Find_p_opt2, FindSCPE2, CalibrateModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16956a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "def FindOffsetRate(G_emp, ε, h̄, ε_pp, h̄_pp, λ_θ, s_pp, σ2_pp, μ_pp, σ2_fp, μ_fp):\n",
    "    p0 = p_emp\n",
    "    l0 = FindEffort(ε_pp, h̄_pp, p0, \"pp\")\n",
    "    β0 = dh(l0, ε_pp, h̄_pp)/(1 - p0)\n",
    "\n",
    "    p1 = Find_p_opt2(*params)\n",
    "    βco = dh(l0, ε_pp, h̄_pp)/(1 - p1)\n",
    "    l1 = FindEffort(ε_pp, h̄_pp, p1, \"pp\")\n",
    "    β1 = dh(l1, ε_pp, h̄_pp)/(1 - p1)\n",
    "    \n",
    "    OffsetRate = 1 - (β1 - β0)/(βco - β0)\n",
    "    return OffsetRate\n",
    "\n",
    "def FindDiffOptSCPE(G_emp, ε, h̄, ε_pp, h̄_pp, λ_θ, s_pp, σ2_pp, μ_pp, σ2_fp, μ_fp):\n",
    "    p_opt = Find_p_opt2(G_emp, ε, h̄, ε_pp, h̄_pp, λ_θ, s_pp, σ2_pp, μ_pp, σ2_fp, μ_fp)\n",
    "    p_SCPE = FindSCPE2(G_emp, ε, h̄, ε_pp, h̄_pp, λ_θ, s_pp, σ2_pp, μ_pp, σ2_fp, μ_fp)\n",
    "    return p_opt - p_SCPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a227eb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Baseline calibration\n",
    "\n",
    "M = [(0.625/0.525)**2, # ratio of variance of log earnings, pp over fp, LMP Figure 5\n",
    "     1,                # share of excess variance explained by bonuses\n",
    "     1.576,            # ratio of mean earnings at two job types, self-calculated following LMP\n",
    "    0.618]             # total variance of log earnings, HT\n",
    "\n",
    "ε = 0.5\n",
    "\n",
    "p_emp = 0.181 # Heathcote Storesletten Violante (2017)\n",
    "\n",
    "# From Heathcote and Tsujiyama (2019)\n",
    "λ_θ = 2.2\n",
    "Y_emp = 77.325 # mean household labor income in 1,000$\n",
    "g_emp = 0.188 # G/Y\n",
    "G_emp = g_emp * Y_emp\n",
    "\n",
    "# From Lemieux MacLeod and Parent (2009)\n",
    "s_pp = 0.45 # share of pp jobs, Figure 4\n",
    "π = 0.23 # probability of receiving a bonus, calculated above based on LMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4780c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prob. of receiving a bonus on a bonus-pay job: 0.48\n"
     ]
    }
   ],
   "source": [
    "# Alternative calculation of the probability of receiving a bonus\n",
    "\n",
    "# Probability of receiving bonus on a bonus-pay job from LMP (based on Table II)\n",
    "# Focus on 90s\n",
    "\n",
    "# share of bonuses-pay jobs in all performance-pay jobs \n",
    "# (adjusting for a number of times the job match was observed)\n",
    "share_b = (30.24 + 6.31)/(37.56 + 7.06)\n",
    "\n",
    "prob_b = (0.57 + share_b - 1)/share_b\n",
    "print(\"Prob. of receiving a bonus on a bonus-pay job:\", np.round(prob_b, decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "727e6707",
   "metadata": {},
   "outputs": [],
   "source": [
    "Calibrations = [] \n",
    "Calibrations.append((M, π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # baseline\n",
    "Calibrations.append((M, π, 1, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # High Frisch\n",
    "Calibrations.append((M, π, 0.1, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # Low Frisch\n",
    "Calibrations.append(([(0.625/0.525)**2, 1 * 0.5, 1.576, 0.618], \n",
    "                     π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # bonuses explain 50% of excess variance\n",
    "Calibrations.append(([(0.625/0.525)**2, 1, 1.576 * 1.25, 0.618], \n",
    "                     π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # 25% higher ratio of mean earnings\n",
    "# Calibrations.append(([(0.625/0.525)**2, 1, 1.576 * 0.75, 0.618], \n",
    "#                      π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # 25% lower ratio of mean earnings\n",
    "Calibrations.append(([(0.625/0.525)**2 * 1.25, 1, 1.576, 0.618], \n",
    "                     π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # 25% higher ratio of variances\n",
    "# Calibrations.append(([(0.625/0.525)**2 * 0.75, 1, 1.576, 0.618], \n",
    "#                      π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # 25% lower ratio of variances\n",
    "Calibrations.append(([(0.625/0.525)**2, 1, 1.576, 0.618], \n",
    "                     0.48, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)) # Higher estimate of probability of receiving performance-pay\n",
    "\n",
    "OffsetRate, DiffOptSCPE = np.zeros(len(Calibrations)), np.zeros(len(Calibrations))\n",
    "for i in range(len(Calibrations)):\n",
    "    params = CalibrateModel(*Calibrations[i])\n",
    "    OffsetRate[i] = FindOffsetRate(*params)\n",
    "    DiffOptSCPE[i] = FindDiffOptSCPE(*params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02d32136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9187823 , 0.91735259, 0.92188163, 0.96547661, 0.92775781,\n",
       "       0.84991033, 0.98251477])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OffsetRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82a7f9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05155078, -0.0489688 , -0.06017402, -0.02808771, -0.04708236,\n",
       "       -0.07455157, -0.04281161])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DiffOptSCPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26e9c365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 20\n",
    "fs2 = 10\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "ax.plot(OffsetRate[0], -DiffOptSCPE[0], 'x', markersize=10, color=\"k\")\n",
    "ax.plot(OffsetRate[1:], -DiffOptSCPE[1:],'o', markersize=10, color=\"k\")\n",
    "\n",
    "ax.annotate(\"baseline calibration\", (OffsetRate[0]+0.003 , -DiffOptSCPE[0]), color=\"k\", size=fs-4)\n",
    "for i in range(1, len(OffsetRate)):\n",
    "    ax.annotate(str(i), (OffsetRate[i]+0.003 , -DiffOptSCPE[i]-0.002), color=\"k\", size=fs-4)\n",
    "    \n",
    "# ax.set_ylim([0, 100])\n",
    "ax.set_yticks([0.03, 0.04, 0.05, 0.06, 0.07])\n",
    "# ax.set_yticks([0.03, 0.05, 0.07])\n",
    "ax.set_yticklabels([\"0.03\", \"0.04\", \"0.05\", \"0.06\", \"0.07\"], fontsize=fs-5)\n",
    "# ax.set_yticklabels([\"0.03\", \"0.05\", \"0.07\"], fontsize=fs-5)\n",
    "# ax.set_ylabel(\"policy error: $p_{SCPE} - p_{optimal}$\", fontsize=fs)\n",
    "ax.set_ylabel(\"policy mistake: SCPE - optimum\", fontsize=fs)\n",
    "# ax.set_ylabel(\"policy error in choosing progressivity\", fontsize=fs)\n",
    "\n",
    "ax.set_xticks([0.84, 0.88, 0.92, 0.96, 1])\n",
    "ax.set_xlim([0.84, 1])\n",
    "ax.set_xticklabels([\"84%\",\"88%\", \"92%\", \"96%\", \"100%\"], fontsize=fs-5)\n",
    "ax.set_xlabel(\"% of crowd-out offset by crowd-in\", fontsize=fs)\n",
    "\n",
    "if update_figs:\n",
    "    plt.savefig('fig5.eps', bbox_inches='tight', format = 'eps', dpi = 100)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2962e2e",
   "metadata": {},
   "source": [
    "### Only performance-pay jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c5e3de6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = CalibrateModel(M, π, ε, s_pp, λ_θ, p_emp, Y_emp, G_emp)\n",
    "ε_pp, h̄_pp = params[3], params[4]\n",
    "σ2_pp, μ_pp, σ2_fp, μ_fp = params[7:]\n",
    "\n",
    "# params2 = params\n",
    "params2 = *params[:6], 1, *params[7:]\n",
    "\n",
    "pV = np.linspace(0, 0.7, 100)\n",
    "SWF = [FindSWF2(p,*params2) for p in pV]\n",
    "\n",
    "p_opt = Find_p_opt2(*params2)\n",
    "SWF_opt = FindSWF2(p_opt, *params2)\n",
    "\n",
    "p_SCPE = FindSCPE2(*params2)\n",
    "SWF_SCPE = FindSWF2(p_SCPE, *params2)\n",
    "\n",
    "SWF_status_quo = FindSWF2(p_emp, *params2)\n",
    "\n",
    "\n",
    "def cons_eq(x, reference=SWF_status_quo):\n",
    "    \"\"\"Translate into consumption equivalent relative to the reference level of SWF\"\"\"\n",
    "    return np.exp(x - reference) - 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "fs = 20\n",
    "fs2 = 20\n",
    "\n",
    "plt.plot(pV, cons_eq(SWF), 'k', lw=3, label=\"SWF in the calibrated model\")\n",
    "plt.plot(p_opt, cons_eq(SWF_opt), marker='o', color='k', markersize=14)\n",
    "textC = \"optimum: p = \" + str(np.round(p_opt, decimals=2)) \n",
    "textC = textC + \"\\nwelfare change: \" + str(np.around(100*cons_eq(SWF_opt), decimals=2)) + \"%\" \n",
    "ax.annotate(textC, (p_opt, cons_eq(SWF_opt)), \n",
    "            xytext=(p_opt-0.1, cons_eq(SWF_opt) + 0.005), color=\"k\", size=fs2)\n",
    "\n",
    "ax.plot(p_SCPE, cons_eq(SWF_SCPE), marker='o', color='k', linestyle=\"none\", markersize=14)\n",
    "textSCPE = \"SCPE: p = \" + str(np.around(p_SCPE, decimals=2)) \n",
    "textSCPE = textSCPE + \"\\nwelfare change: \" + str(np.around(100*cons_eq(SWF_SCPE), decimals=2)) + \"%\" \n",
    "ax.annotate(textSCPE, (p_SCPE, cons_eq(SWF_SCPE)), \n",
    "            xytext=(p_SCPE + 0.01, cons_eq(SWF_SCPE)), color=\"k\", size=fs2)\n",
    "\n",
    "ax.plot(p_emp, 0, marker='o', color='black', linestyle=\"none\", markersize=14)\n",
    "text_status_quo = \"status quo\\n p = \" + str(np.around(p_emp, decimals=3)) \n",
    "ax.annotate(text_status_quo, (p_emp, 0), \n",
    "            xytext=(p_emp - 0.07, 0.006), color=\"black\", size=fs2)\n",
    "\n",
    "ax.set_xlim([0.1, 0.6])\n",
    "xticksv = np.linspace(0.1, 0.6, 6)\n",
    "ax.set_xticks(xticksv)\n",
    "ax.set_xticklabels([str(np.round(x, decimals=1)) for x in xticksv], fontsize=fs-2)\n",
    "\n",
    "ax.set_xlabel('progressivity rate $p$', fontsize=fs)\n",
    "\n",
    "ax.set_ylim([-0.02, 0.06])\n",
    "ax.set_yticks([-0.04, -0.02,  0, 0.02, 0.04, 0.06])\n",
    "ax.set_yticklabels([\"-4%\", \"-2%\",\"0%\",\"2%\", \"4%\", \"6%\"], fontsize=fs-2)\n",
    "ax.set_ylabel('welfare change (% of cons.)', fontsize=fs)\n",
    "\n",
    "# plt.legend(loc = (0,1), frameon=False, fontsize=fs2)\n",
    "if update_figs:\n",
    "    plt.savefig('fig2b.eps', bbox_inches='tight', format = 'eps', dpi = 100)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
